Alexa, Do Men Talk Too Much?
Who is this presentation for?Women in Technology, Men in Technology, Data Scientists
What you'll learn
Numerous studies 1 have shown that men consistently interrupt women in professional and non-professional settings. There is also a tendency to provide remedial explanations to concepts that are already well known to the target audience. A new word “Mansplaining” is added to Merriam-Webster dictionary that captures this social problem 2. At its core, mansplaining is when a man talks condescendingly to someone (especially a woman) about something he has incomplete knowledge of, with the mistaken assumption that he knows more about it than the person he is talking to does.
This phenomenon impacts women in all professions, at all levels of their career. Any form of privileged explaining can cause women to be uncomfortable in their own careers and can keep women from entering male-dominated fields such as STEM. For women already in these fields, being talked over in a professional setting like a meeting, is not just frustrating but could be career limiting.
The purpose of this presentation is two-fold. First, we build an Artificial Intelligence (AI) and Machine Learning (ML) based solution that explores the prevalence of mansplaining in professional settings. Then we discuss strategies that can be employed to addresses the issue at hand. The goal is to demonstrate the feasibility of using the combination of technology and human actions to raise awareness about and address prevalent issues that have direct impact on women in technology.
For the technical solution, we use Kaggle ‘Gender Recognition by Voice’ dataset 3. Machine learning (ML), using algorithms that continuously assess and learn from data, enables building automated analytical models that provides insights into hidden patterns in the data. Artificial Intelligence (AI) based natural language processing (NLP) and automatic speech recognition (ASR) provides the ability to access these insights in a conversational manner.
We use the combination of ML and AI to build an Alexa Skill, that can be used to explore questions such as “Do men really talk more than women in meetings?”, “How many women speak up in the meetings vs men?”, “What is the percentage of women participating in a meeting?”.
The technical solution helps in identifying and raising awareness of the issue, but doesn’t solve the problem around gender identity and equality in professional settings. Then what are some what are some strategies that both women and men can take to address this imbalance?
We will discuss some strategies for women such as owning the responsibility to have their voices heard by speaking up, asking questions, being an ally to other women as well as ensuring we are mentoring young girls and women in technology. We will also discuss a few strategies for men, such as being empathetic and realizing that the problem is real, taking feedback from the female colleagues about the issue in order to be more self aware during interactions.
What makes this presentation crucial now? The number of women in technology is in a downward trend. Just in the field of computer science, in 1984 women held 37% of degrees. Today that number is down to 19%. Even when women do choose STEM careers, only 26% work in technical roles, compared to 40% of men. In technology, women leave the industry at a rate 45% higher than their male peers. Addressing the gender identify and gender equality issues at workplace is a crucial to reverse the trend.
Amazon Web Services
Sireesha is a Solutions Architect at Amazon Web Services (AWS), with Area of Depth is Machine Learning and Artificial Intelligence. She provides guidance to AWS customers on their ML/AI workloads. While working full-time, Sireesha earned her Ph.D in May 2013 and Post Doctorate in 2015 from University of Colorado, Colorado Springs. Her Ph.D thesis is, “Multi-tier Internet Service Management using Statistical Learning Techniques (https://dspace.library.colostate.edu/bitstream/handle/10976/264/CUCS2013100001ETDSPECS.pdf?sequence=1.)”. She led the Colorado University team in winning and successfully completing a 2-year research grant from Air Force Research Lab on “Autonomous Job Scheduling in Unmanned Aerial Vehicles”. She is an experienced public speaker and has presented research papers at International Conferences: CoSAC: Coordinated Session-Based Admission Control for Multi-Tier Internet Applications (https://www.researchgate.net/publication/221092402_CoSAC_Coordinated_Session-Based_Admission_Control_for_Multi-Tier_Internet_Applications) at IEEE Int’l Conf. on Computer Communications and Networks (ICCCN), 2009; Regression Based Multi-tier Resource Provisioning for Session Slowdown Guarantees (https://www.researchgate.net/publication/220780958_Regression_Based_Multi-tier_Resource_Provisioning_for_Session_Slowdown_Guarantees) at IEEE Int’l Conf. on Performance, Computing and Communications (IPCCC), 2010. She also published technical articles : Coordinated session-based admission control with statistical learning for multi-tier internet applications (https://www.researchgate.net/publication/222549520_Coordinated_session-based_admission_control_with_statistical_learning_for_multi-tier_internet_applications) in Journal of Network and Computer Applications (JNCA);Regression-based resource provisioning for session slowdown guarantee in multi-tier Internet servers (https://www.researchgate.net/publication/220379377_Regression-based_resource_provisioning_for_session_slowdown_guarantee_in_multi-tier_Internet_servers) and Multi-tier Service Differentiation: Coordinated Resource Provisioning and Admission Control (https://www.researchgate.net/publication/260042453_Multi-tier_Service_Differentiation_Coordinated_Resource_Provisioning_and_Admission_Control) in Journal of Parallel and Distributed Computing (JPDC)
Amazon Web Services
Shelbee Eigenbrode is a Solutions Architect at Amazon Web Services (AWS). Her current areas of depth include DevOps combined with Machine Learning/Artificial Intelligence. She has been in technology for 22 years spanning multiple roles and technologies. She spent 20+ years at IBM and joined AWS in November of 2018. She is a published author, blogger/vlogger evangelizing DevOps practices with a passion for driving rapid innovation and optimization at scale. In 2016, she won the DevOps Dozen Blog of the year (https://devops.com/the-2016-devops-dozen-winners-announced/)demonstrating what DevOps Is Not. With over 26 patents granted across various technology domains, her passion for continuous innovation combined with a love of all things data has recently turned her focus to the field of Data Science. Combining her backgrounds in Data, DevOps and Machine Learning, her current passion is to help customers not only embrace data science but also to ensure all data models have a path to being utilized. She also aims to put ML is the hands of developers and customers that are not classically trained data scientists.
Amazon Web Services
- Emily is a Machine Learning Specialist Solutions Architect at Amazon Web Services (AWS). She has been leading data science projects for many years, piloting the application of machine learning into such diverse areas as social media violence detection, economic policy evaluation, computer vision, reinforcement learning, IOT, drone, and robotic design. Her master’s degree is from the University of Chicago, where she developed new applications of machine learning for public policy research with the Data Science for Social Good Fellowship. She has worked as a data scientist at the Federal Reserve Bank of Chicago, and as a solutions architect for an explainable AI start-up in Chicago. At AWS she guides customers from project ideation to full deployment, focusing on Amazon SageMaker. Her customers are household names across the world, such as TMobile.
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